{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sdm as sdmlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bits = 1000\n",
    "sample = 1000000\n",
    "scanner_type = sdmlib.SDM_SCANNER_THREAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "address_space = sdmlib.AddressSpace.init_random(bits, sample)\n",
    "counter = sdmlib.Counter.create_file('sdm-tmp-DELETE-IT', bits, sample)\n",
    "sdm = sdmlib.SDM(address_space, counter, 451, scanner_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def write(n=1000, prefix=''):\n",
    "    for i in xrange(n):\n",
    "        if i%20 == 0:\n",
    "            clear_output(wait=True)\n",
    "            print '{}Writing {} random bitstrings: {:4d} ({:.2f}%)'.format(prefix, n, i+1, 100.*(i+1)/n)\n",
    "        b = sdmlib.Bitstring.init_random(bits)\n",
    "        sdm.write(b, b)\n",
    "    return n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_distances(bs, k=6, sample=1, step=1, prefix=''):\n",
    "    distances = []\n",
    "    x = range(0, bits+1, step)\n",
    "    for i, dist in enumerate(x):\n",
    "        if i%20 == 0:\n",
    "            clear_output(wait=True)\n",
    "            print '{}Distance: {:4d} ({:.2f}%)'.format(prefix, dist, 100.*(i+1)/len(x))\n",
    "        v1 = []\n",
    "        for _ in xrange(sample):\n",
    "            c = sdmlib.Bitstring.init_from_bitstring(bs)\n",
    "            c.flip_random_bits(dist)\n",
    "            assert c.distance_to(bs) == dist\n",
    "\n",
    "            v2 = []\n",
    "            v2.append(c.distance_to(bs))\n",
    "            d = c\n",
    "            for j in xrange(k):\n",
    "                d = sdm.read(d)\n",
    "                v2.append(d.distance_to(bs))\n",
    "            v1.append(v2)\n",
    "        distances.append([dist, v1])\n",
    "    return distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_ref = sdmlib.Bitstring.init_random(bits)\n",
    "sdm.write(bs_ref, bs_ref)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14000] Distance: 1000 (100.00%)\n"
     ]
    }
   ],
   "source": [
    "distances = []\n",
    "qty = 1000\n",
    "n = 50\n",
    "step = 1\n",
    "sample = 4\n",
    "k = 6\n",
    "total = 0\n",
    "for i in xrange(n):\n",
    "    prefix = '[{}] '.format(total)\n",
    "    total += write(qty, prefix=prefix)\n",
    "    v = calculate_distances(bs_ref, k=k, sample=sample, step=step, prefix=prefix)\n",
    "    distances.append((total, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(data, iter_readings):\n",
    "    # Make plot with vertical (default) colorbar\n",
    "    maxd = int(data.max())\n",
    "    mind = int(data.min())\n",
    "    avgd = int ((maxd+mind)/2);\n",
    "    fig = plt.figure(figsize=(8, 6), dpi=100)\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    #use aspect=20 when N=1000\n",
    "    #use aspect=5 when N=256\n",
    "    \n",
    "    from matplotlib.pylab import cm, contourf, contour\n",
    "    \n",
    "    cax = ax.imshow(data, cmap=cm.YlGnBu, aspect='auto', interpolation=None, norm=None, origin='lower')\n",
    "    ax.grid(True, label='Distance')\n",
    "\n",
    "    # Add colorbar, make sure to specify tick locations to match desired ticklabels\n",
    "    cbar = fig.colorbar(cax, ticks=[mind, avgd, maxd]) #had ZERO here before\n",
    "    cbar.ax.set_yticklabels([str(mind), str(avgd), str(maxd)])\n",
    "    cbar.ax.set_ylabel(u'Distance obtained after {} iteractive-readings'.format(iter_readings), fontsize=12)\n",
    "        \n",
    "    #########CONTOUR DELINEATES THE CRITICAL DISTANCE\n",
    "\n",
    "    # We are using automatic selection of contour levels;\n",
    "    # this is usually not such a good idea, because they don't\n",
    "    # occur on nice boundaries, but we do it here for purposes\n",
    "    # of illustration.\n",
    "    #CS = ax.contourf(data, 100, levels=[mind,avgd,maxd], alpha=0.1, cmap=cm.YlGnBu, origin='lower')\n",
    "    \n",
    "    # Note that in the following, we explicitly pass in a subset of\n",
    "    # the contour levels used for the filled contours.  Alternatively,\n",
    "    # We could pass in additional levels to provide extra resolution,\n",
    "    # or leave out the levels kwarg to use all of the original levels.\n",
    "    #CS2 = ax.contour(CS,  levels=[88], colors='gray', origin='lower', hold='on', linestyles='dashdot')\n",
    "    #CS2 = ax.contour(data,  levels=[1,2,3,4,5,6,7,8,9,10], colors='gray', origin='lower', hold='on', linestyles='dashdot')\n",
    "    \n",
    "    plt.title('Critical Distance Behavior', fontsize=20)\n",
    "    plt.xlabel('Original distance', fontsize=12)\n",
    "    plt.ylabel('# items previously stored (000\\'s)', fontsize=12)\n",
    "    # Add the contour line levels to the colorbar\n",
    "    #cbar.add_lines(CS2)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(distances, k):\n",
    "    # k = iterative readings\n",
    "    \n",
    "    # [(x, y, z)]\n",
    "    # x = old distance\n",
    "    # y = number of writings\n",
    "    # z = new distance\n",
    "    data = []\n",
    "    for writings, v1 in distances:\n",
    "        v = []\n",
    "        for old, v2 in v1:\n",
    "            new = sum(x[k] for x in v2)/len(v2)\n",
    "            #data.append([old, writings, new])\n",
    "            v.append(new)\n",
    "        data.append(v)\n",
    "    return np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d46bed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 6\n",
    "data = get_data(distances, k)\n",
    "#data = get_data(distances2, k)\n",
    "plot_heatmap(data, k)\n",
    "#print len(data), len(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
